Hello @jjsgva,
Thank you for asking, and my apology for the much-delayed reply.
And thanks, @RobbL, for bring my attention to this inquiry.
So let me try to address the question between the difference between community and social networks.
First of all, I’m afraid to say that there is no academic reference to reference for the specific distinction I made between communities and social networks, because I believe I was the first to characterize the difference this particular way. However, I didn’t just make them up out of thin air. There is an academic paper trail, and very good reasons for my particular definition of community vs. social network, which is based on their mathematical structural properties. However, this mathematical/structural definition does require a bit of technicality and a bit of background knowledge in sociology and network/graph theory to fully understand. So, to avoid creating more confusion with an esoteric definition, I decided to create a definition that is much easier to understand by the layman, that is fully consistent with the more rigorous mathematical definition.
If you bear with me, I’ll go through the logic with you.
As I said earlier, the particular distinction I used between community and social network is a structural one. Keep in mind that there are many other ways to distinguish these 2 social structures (e.g. use case, technology, business applications, or even public perception, etc.). No one is particularly optimal, but some are better suited for different purposes. However, I choose to use their structural properties because they are general and more fundamental to the way people communicate.
The critical concept to understand is transitive closure (which is related to the more familiar triadic closure property in sociology).
Community is a social structure that satisfies the mathematical property of transitive closure. If [person A] is in the same community as [person B], and [person B] is in the same community as [person C], then [person A] will be in the same community as [person C].
Social network, on the other hand, breaks transitive closure (does not satisfy transitive closure). That is, if [person A] is connected to [person B], and [person B] is connected to [person C], then it is not necessarily the case that [person A] will be connected to [person C].
This structural property limits how members of the community/social network can communicate and therefore interact.
In the case of a community, there is no structural communication barrier between [person A] and [person C]. That means in a community, anyone CAN talk to everyone else as long as they are in the same community. A YouTube user can talk to anyone on the YouTube platform. This doesn’t mean that everyone WILL talk to everyone else, because there may still be other barriers to communication (e.g. social, behavioral, psychological, geographic, linguistic, and even political) that prevent people within the same community from communicating. These are very interesting in their own right, but beyond the scope of this reply.
With this definition, many social media channels are really communities, and they are typically referred to, in academia, as communities of interest.
In contrast, a social network will have some structural communication barrier between [person A] and [person C]. If they are not connected, they CAN’T communicate or interact as freely. Exactly how much interaction and communication is allowed will be determined by the particular platform. But there will be some structural barrier. For example, you can’t message people on Facebook unless you are connected to them first, but you can still view their public profile and photos, etc.
In this definition, Facebook, Linkedin, as well as many mobile messengers (e.g. WhatsApp, Line, WeChat, etc.) are structurally social networks based on this definition.
See the distinction between them?
As I mentioned earlier, the transitive closure property is related to the more well-known studied triadic closure property on social network. Triadic Closure simply says that if 2 persons (e.g. [person A] and [person C]) are connected to the same person (e.g. [person B]), then there is a higher probability that they are connected to each other. However, this connection is not guaranteed, otherwise, we have transitive closure, in which case we’ll get a community.
In many classical sociology literatures that study social networks, the part of the network that has a significantly higher degree of closure is precisely what people defined as a community (Coleman 1998). Basically, regions that are more densely connected (i.e. have a higher degree of closure or triadic closure) than their surrounding in a social network is what constitutes a community and what people defined as a community. This is not just a theoretical exercise, as people have developed community detection algorithms that use this property to operationally discover communities within a social network (Newman 2006).
OK, I hope this convinces you that there is truly a structural distinction between community and social networks, and this is documented in academic literature. But as you can see, this does require quite a bit of explaining, and it’s probably overly academic for most business audience.
So I’ve decided to create a simpler definition that is fully consistent with this academically rigorous definition. This is what lead to my definition in the post that characterizes the difference between community and social network based on what held them together. This definition is consistent with the mathematically rigorous definition because they are virtually equivalent.
A community is held together by a common interest. So if [person A] have the same interest as [person B], and [person B] have the same interest as [person C], then [person A] will have the same interest as [person C], satisfying the transitive closure property. So they will be in the same community and thus won’t have any structural communication barrier among them.
Social networks, on the other hand, is held together by interpersonal relationships. Given that [person A] has a personal relationship with [person B], and [person B] has a relationship with [person C], this does not guarantee that [person A] has a personal relationship with [person C], thus breaking the transitive closure property, as social networks should (which creates a structural barrier to communication).
This came out much longer, but the short answer to your original question is, NO. There aren't any academic papers that you can cite that characterizes community vs social network based on what held them together. But there is a very rigorous and academic reason that I define the difference between community and social network the way I did. I hope you can use this resource to help further your study of community vs social network.
Thank you for your interest.
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With technology continually evolving and changing, so does its vocabulary. The enterprise world is littered with jargon, one of the buzzwords du jour being “digital transformation (DT)” which I’m sure you’ve heard of by now. But what does it mean? It’s like Dan Ariely’s humorous comment on big data, “everyone talks about it, nobody really knows how to do it, but everyone thinks everyone else is doing it, so everyone claims they are doing it.”
At a high level, DT is very easy. It’s simply the adoption of digital technologies to transform your business. So just choose the digital technology you want, and use it to change how your business operates. Done!
Why Digital Transformation Fails
Sounds easy. But it’s not. Numerous sources report that roughly 70% (ranging from 66% to as high as 84% via Forbes) of the DT initiatives fail. Clearly, it can’t be as simple as deploying a digital technology (given such high failure rate), even though that could pose challenge in some cases.
So why is DT so difficult? The reason is because that a true transformation of your business requires more than just the adoption of new technology. DT usually starts with some kind of technology upgrade, but that’s only the first step. Subsequently, it requires changes in your business processes, your employee and leadership behavior, and ultimately your corporate culture. Changing technology might be easy, but changing the people, processes and culture is hard.
The challenge of DT is not a digital or even a technological problem; it’s a business transformation problem. If we try to understand why DT fails, the most common causes of failure boil down to the following 4 categories of reasons.
Technology:
Using outdated technologies
Failure to integrate with legacy or other digital systems
Believing that it’s only a technology problem
People:
Lack of clarity and vision
Lack of leadership support
Too much top down imposition without grass root support
Lack of a digitally savvy workforce
Process:
Silo effort that didn’t engage the broader stakeholders
Process misalignment
Not agile enough for faster innovation
Culture:
Short term thinking
Not customer centric
Too little cross-functional collaboration
Since these are failure modes, they are all important. As it only takes one broken link to break the whole chain, any one of these failure modes could undermine the success of your entire DT initiative. So every one of them must be addressed, which is a lot for businesses to undertake.
But here’s the bright side: Although all the common failure modes must be addressed, not all of them need to be addressed at once. And if you are embarking on the DT journey, not all of them need to be addressed at the beginning. So which ones should you focus on first?
Upon analyzing the natural dependency among these failure modes, there are only 3 that must be addressed from the get-go. And I will explain this with the video blog below.
1) Customer Centricity
A customer-centric strategy is imperative, simply because every business needs customers. Moreover, in an increasingly service-oriented subscription economy, every business is striving to retain their customers, because not only is the competition more intense, the switching cost for consumers is often minimal. While this is a given from a business standpoint, customer centricity is equally as important for your digital transformation (DT) initiative for several reasons.
It’s easier to rally for support when you have a customer-centric strategy, precisely because it makes business sense. Very few people would argue against serving your customers. A well thought-out customer-centric strategy could easily win both leadership and grassroot support. You still need to sell the strategy within your enterprise, but it shouldn’t be a difficult sell.
It’s also less challenging to create processes that are aligned across different departments with a customer-centric mindset. Traditional business processes are often created to optimize some business KPIs while meeting their operating constraints. However, different departments and teams often operate under disparate constraints and have unique set of KPIs. Consequently, their processes are typically misaligned because they were created irrespective of one another. Customer-centricity serves as the glue that binds different departments and teams together. It helps you create processes that are aligned with giving your customers a great experience.
When all your processes are aligned, it facilitates cross-functional collaboration. At the very least, the processes are not adding friction that could hinder collaboration. Although this doesn’t automatically drive collaboration, it certainly makes it easier when there is a business need to do so. When that happens, your DT is suddenly no longer a siloed effort.
Finally, a customer-centric mindset fosters long-term thinking because most businesses want to have loyal (long-term) customers, especially in a subscription economy.
2) A Clear Vision
Despite the simplicity of the definition, digital transformation (DT) could be confusing because it’s different for every company. Myriads of digital technologies are on the market, which can change any one of the multitude of business operation within your enterprise.
For example, DT for one company may be using iPads (a digital technology) to scale onboarding of new employees (a perfectly valid HR function). It could also be using social media (another digital technology) to engage and support your customers throughout their customer journey (a marketing and customer support operation). It could even be using big data (yet another class of digital technology) to predict sales, using IoT and augmented reality to improve customer experience, or anything in between.
DT can mean many different things, so you must have a clear vision of what DT means for your enterprise. Which digital technology are you using? And which part of your business operation are you trying to improve with these technologies initially? Most importantly, what business outcome are you trying to achieve? As alluded earlier, a customer-centric mindset could help you answer some of these questions and shape your vision.
Armed with a clear vision of what DT means for your business makes it even easier to garner both leadership and grassroot support. And if you are a leader, a clear vision probably means that you are bought in and committed to supporting this change.
3) The Right Technology
Since digital transformation (DT) almost always starts with a technology upgrade, it is important to choose the right technology at the beginning. Having a clear DT vision that is customer-centric helps you choose the digital technologies to realize your vision, but there are other factors to consider.
Certainly, the right technology must have all the functionality required by your specific DT project. It must meet all the security, reliability, and legal compliances for your enterprise, and must built to scale with robust technologies that last. This is unique to each business, but there are two elements that are often overlooked at the beginning which may impact the long-term success of your DT initiatives.
First, the right technologies should be easily integrated into with the rest of your company’s technology ecosystem. And that includes both your legacy systems and other newly adopted digital systems. Keep in mind that when you kick off a digital initiative, your core business will still be running on your legacy system. Failing to integrate with these systems means your DT project will remain a siloed effort. While DT initiatives often start small in one area of the company, it must permeate throughout your enterprise to achieve lasting transformation.
Second, the right technologies should be simple and intuitive to use. It should be so intuitive that even your non-digital workforce should be able to pick it up and immediately carry out rudimentary functions without much training. Of course, training and education will always be required to reach proficiency.
The key is to make sure that the learning curve does not offset the efficiency gain from the use of your new digital technology for the “digital novice,” even at the very beginning. Furthermore, when there is residual efficiency gain, even during the adoption phase of your DT project, innovative minds within your enterprise will have the cognitive surplus to innovate and be more agile.
Transformation means lasting change
Digital transformation is a journey. It always starts with the adoption of digital technologies, but it must also change the people, process and the culture to be truly transformative. It typically begins as a siloed technology project, but must permeate throughout your enterprise. Although digital transformation can seem difficult, concentrating on the above focuses at the very start will help pave the road for long-term success.
*This article originally appeared on CMSWire.
*Image Credit: Pexels and tpsdave.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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We’ve been told that we are special ever since we were little, but how special are we? I challenge you to find another person in the world that is exactly like you. Such person probably doesn’t exist. So if that is the case the question is, why is our brand experience often identical to everyone else’s? Somehow, in the eyes of big brands, we are just like everyone else.
The Look-Alike Illusion
Let’s try to understand why that’s the case. If you were to find your double, how would you do it? You may start the search based on some personal attributes like, gender, age, ethnicity, etc. However, as you include more of these demographic dimensions (e.g. religion, education level, political orientation, etc.), you will discover that fewer are like you (i.e. matches you in every one of these dimension).
So the apparent similarity among consumers is merely an illusion that arises from the lack of data. We only look alike because brands don’t have enough data to distinguish us uniquely. With more data, brands can see us in 3D and recognize our uniqueness.
Every one of your customers are unique, just like you. They all have different needs, limitations and preferences. They all deserve a unique brand experience. The question is, how?
In order to personalize the experiences of your customers, brands must overcome two major challenges:
Brands must understand their customers at a personal level. This requires huge amounts of personal and behavior data about their customers to uniquely distinguish them all, which can be difficult to obtain.
Brands must deliver a unique experience for each customer base on the understanding of their individual preferences. This is even more challenging, because most brands operate at scale for efficiency (due to the economy of scale). Such individualized offerings are very difficult to scale.
The Digital Advantage of Big Data
Personalization is hard, because traditionally many brands don’t have enough data to understand their customers at a personal level, let alone deliver a unique experience.
Big data changes this. For the first time, brands have enough data to distinguish one customer from another. Beyond the demographic dimensions on which traditional segmentation is based, brand now have access to hundreds and thousands of social and behavior dimensions. So brands have enough data to overcome the first personalization challenge.
This is why brands like Amazon and Netflix is able to hyper-personalize (i.e. personalize to one single individual) and offer a truly unique experience to each customer. However, many brick-and-mortar retail brands seem to be lagging behind in their personalization efforts. This is not surprising, and there are good reasons for it. Digital-native brands have a huge advantage in overcoming both challenges of personalization:
In the digital world, it is much easier to collect lots of behavior data from the consumers. Digital-native brands can easily track what product you searched, browsed, bought; what movies you rated well, what movies you watched, how much time you spent on researching a particular product, and other behavior. However, it is much more difficult for brick-and-mortar shops to obtain data on who visited the store and what items they browsed, tried on or other actions.
Not surprisingly, it is also much easier to deliver a unique experienced in the digital world. This is because most of the digital environment is controlled by software and can be customized by data learned from a consumer’s behaviors. Everything from which product you see, to what background color is used in the e-store can all be customized by a customer’s preferences and few lines of codes. In the physical world, it would be impossible to manipulate the experience suite each and every individual.
This is one of the reason why so many companies are talking about digital transformation today. Because the digital-native enterprises are able to give their customers a much more personalized experience, they will win the long-term engagement with their customers. This not only enables them to acquire customers faster, but also retain them much more effectively. This is vital in an increasingly competitive market for people’s limited attention.
Ubiquitous Sensing via IoT
Due to the digital advantage, retail brands face serious challenges from their digital competitors. But this is all about to change. The Internet of Things (IoT) will enable a whole new level of behavior data collection like never before. When physical “things” in this world are able to communicate with each other, it offsets the advantages that digital brands have in understanding their customers. A pair of jeans on the shelf may one day know which mobile device is looking at them, which picked them up, and which actually tried them on.
Not only does IoT enable the collection of behavior data in the physical world, it also enables the collection of rich environmental metadata, which gives the context and meaning to the behavior. The metadata gives contextual cues that help brands understand why a consumer behaved the way he or she did, rather than just the fact that he or she did something.
For example, knowing that I bought a GoPro is one level of understanding, but knowing that I bought it with my niece for her birthday should completely change the way brands market future product to me. I wouldn’t see any irrelevant ads on GoPro accessories for a camera that I don’t own. Instead, an annual reminder of my niece’s upcoming birthday, and potential accessories for young women as birthday present can go much further.
Merging the digital behavior data with the physical give us a more complete 3D view of our customers. This will help us further personalize the experience of our customers at every touchpoint along their consumer journey.
The Retail Strike Back with AR
The ability to measure, track and understand customers at a personal level is only half of the battle. The other half is even more challenging for brick-and-mortar retail brands: delivering a unique experience based on their understanding of the customer’s preference. This is difficult because manipulating physical spaces and environment to meet any individual’s preference is nearly impossible. That is, until augmented reality (AR), which has been popularized by Pokemon Go.
Tomorrow’s consumers do not have to see the physical world as-is, they can overlay it with digital layers. Although it’s impossible to customize the items on a physical shelf to suite everyone’s preference, it is possible for AR to recognize items that are relevant to consumers and direct their attention to them. While it is not realistic to paint the wall of the retail space with everyone’s favorite color, AR can overlay the walls with any color or even background of a customer’s choosing.
AR provides brick-and-mortar brands with a digital layer on top of the physical world. This offsets the advantages that digital brands have in delivering a unique experience again. Just as in the purely-digital world, this digital layer is controlled by software and can be customized by code and as much preference data as consumers are comfortable providing.
Although the digital-native brands have a head start in personalization, technological innovations such as IoT and AR, will even the playing field. Eventually, every brand will have the capability to hyper-personalize their experience for everyone. Personalization is not merely a set of technologies, it is a customer-centric business strategy that recognizes the unique context of every individual customer.
Brands, digital or not, must learn to consider individual preferences in order to win customers’ long-term engagement. Besides, brands have no excuse not to provide us a personalized experience. All the required technologies already exist, for brands in both the digital and physical world. With this, perhaps one day we may have a personalized shopping experience like those in the movie Minority Report.
*This article originally appeared on CMSWire.
Image Credit: Unsplash, geralt, and Keiichi Matsuda.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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After a deep dive into the inner workings of one class of prescriptive analytics—recommender system (personalization engines), it’s time to step back and explore how we can leverage prescriptive analytics in general. Today, I am going to outline 3 relevant use cases:
In business, prescriptive analytics can help you optimize business processes and operation
In social business, it can help your social media team focus on what drives the biggest business impact
In social media, it can even help you optimize your engagement time
These are very distinctive use cases, where the objectives being optimized are drastically different. I hope these examples will not only demonstrate the power and versatility of prescriptive analytics, but also give you a better understanding of how they work. (If they don’t please feel free to let me know!)
Optimization of Business Process and Operation
I’ve discussed before that the simplest example of prescriptive analytics is a GPS, which operates in the geospatial domain. In general, prescriptive analytics are not limited to geospatial optimization. We can optimize processes and other parts of business operations. One of the most common use cases of prescriptive analytics is business optimization. Since the underlying computation of prescriptive analytics is already an optimization of some objective, this application is very natural. In this case, the objective we optimize is typically the efficiency or throughput of the process.
Whether you are optimizing a business process in marketing, sales, or customer service, you must tell the prescriptive analytics system what you are trying to achieve. This is like telling the GPS where you are going. For example, increase conversion by 10%, increase sales by 20%, or increase your net promoter score (NPS) by 5 points. These are the goals, or “destinations” you are trying to reach in a non-geospatial domain.
Subsequently, the prescriptive analytics system would prescribe a sequence of actions that lead to the corresponding business outcomes you want (i.e. increase conversion by 10%, sales by 20%, or NPS by 5 points). For example, to achieve 10% conversion lift, the system may prescribe reducing the frequency of your email marketing by 35%; simultaneously increase your real-time social media engagement by 30%; and when your real-time engagement reaches 15%, start directing people to your customer community for peer-to-peer engagement and recommendation. These are like the turns that your GPS system advises you during the journey, except these directions are not in the geospatial domain.
Optimizing Business Impact of Your Social Media Engagement
Now you know what prescriptive analytics can do for your business, let’s dive deeper on how it prescribes actions. The key lies in the objective that’s being optimized. Let’s examine a couple of examples from social media.
These days, social media is not new to business, but many enterprises still struggle to figure out how best to leverage it. Because there are so many different things you can do on social media, it’s hard to determine where to best allocate your limited resources (e.g. time, money, energy, etc.). Should you create more YouTube videos or should you use Snapchat? Should you publish more blogs, or participate more in the Q&A section of your community? The social media landscape is complex with thousands of social channels. Even within a single social channels, there are probably a handful of actions you can take. Take Twitter for example. It’s probably one of the simplest social channels out there, but you can already engage in many different ways: tweet a message, reply to one, retweet it, favorite it, follow someone, or simply read the tweets coming out of the firehose. This gives rise to many different social metrics that quantify how you engaged on social media.
Prescriptive analytics can help you focus on what you should do to achieve the biggest impact, but you must tell it what kind of impact you are looking for. Whether it’s increasing marketing conversion, sales, or customer satisfaction (CSAT), you should be able to measure the impact you want to drive. This is typically a key performance indicator (KPI) for your business. Once you have the KPI that you are trying to drive, and the social media metrics that describe how you engage, it’s relatively trivial to perform a time-lagged cross correlation analysis to see which social metrics have the strongest correlation with your KPI. Although correlation is better than flying blind, it would be better if we could establish causation as well. But this would require less trivial methods in statistics or econometrics (e.g. instrumental variables). If you are comfortable with these advanced techniques, you can even establish causal relationship and identify the strongest causal predictors for your KPI.
The correlation strength (causal or not) between the social metrics and KPI is the objective. Upon maximizing this objective, the prescriptive analytics system would be able to prescribe the actions that are most effective at driving your KPI. This helps you focus your effort on a few actions that will give you greatest impact (as measured by the KPI). And if you choose a different KPI, the system will prescribe a different set of predictors that maximize this objective.
When to Post on Social Media—Engagement Time Optimization
Because social media engagement is voluntary, people can participate anytime they want. But when is the best time to participate? The answer really depends on what you are trying to achieve. The goal of most social media participation is usually to reach the widest audience (whether that’s for a brand or for us personally). Even when you are posting a question and want the fastest answer or the most accurate answer, these goals can often be achieved indirectly by maximizing your reach. By reaching the widest audience, you increase your odds of reaching someone who can respond immediately; and by reaching the widest audience, you also increase your odds of finding someone who has the expertise to address your question accurately.
There have been numerous studies on when is the optimal time to post on social media, and many infographics provide general guidance on when to post on various social channels. In general, these studies are rather limited because the data is highly aggregated, the sample size small, and the methodology not rigorous enough. We recognized that there really isn’t a universal best time to post on social media, because the best time to post is ultimately specific and unique to the individual.
Our brilliant data science team (humble brag!) have analyzed over a billion posted messages and observed reactions and found that the best time to post depends strongly on your specific audience’s engagement profile. Meaning when your specific audience is most actively participating on a particular social channel. Keep in mind that it is your audience’s behavior (i.e. how they choose to use social media) that determines the optimal time to post for you. Since we all have different audience on social media (e.g. different followers, friends, connections, etc.), the best-time-to-post for you may be totally different from the best-time-to-post for me. This is precisely how Lithium Reach is able to recommend the best-time-to-post that is hyper-personalized to a person or a brand.
Now, I could certainly tell you more about the product, but that’s not my style. However, if you have specific product related questions, I’d be happy to discuss, or invite more qualified product management staff to chime in.
We further validated the optimality of our hyper-personalized recommendation on a sample of half million active users and more than 25 million messages observed over a 56-days period. We found that our individually optimized post-time leads to an average of +17% engagement lift on Facebook and +4% on Twitter. This is a VERY conservative average. In practice, we have seen more than +50% engagement lift at times from brands using Lithium Reach’s recommended time-to-post feature.
Conclusion
We discussed 3 business use cases of prescriptive analytics. In each case some objectives are optimized, so the system can prescribe a few actions (or sequence of actions) out of infinite number of possible action you can take.
By maximizing the efficiency or throughput of some business processes under appropriate resource constraint, we get a prescribed sequence of steps to help us optimize the process.
By maximizing the causal correlation strength between social metrics and your desired KPI, we get a prescribed set of social metrics we should focus on driving. Although there are hundreds and possibly thousands of social metrics, this set of metrics is most effective at moving the needles for your KPI.
By maximizes the engagement profile of your specific audience (e.g. follower, friends, connections, etc.), we get a set of personalized recommendations on when to post on social media. Although you can post social media content at any time you like, posting at the recommended times will maximize the reach of your messages.
While optimization is something that computers can do very efficiently, doing this at the individual level is still a challenging big data problem. Because people’s social interactions change continuously, we must constantly re-optimize as the new data arrive from the respective social streams. Fortunately, our data scientists have done all the heavy lifting and built this hyper-personalized post time recommendation algorithm into Lithium Reach. Now, you can get the benefit of personalized recommendation on when to post without needing to routinely analyze billions of message and their reactions.
As you can see, prescriptive analytics is versatile and powerful. It has just as many applications in science and engineering as in business and social media. Next time I’ll share a somewhat esoteric use case of prescriptive analytics in data science. Yes, data scientists like me also use prescriptive analytics. Come back next time to find out more!
Image Credit: geralt, geralt, and valentinsimon0.
Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.
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